Computer Vision for Elderly Care Based on Hand Gestures

Oudah, Munir; Al-Naji, Ali; Chahl, Javaan

doi:10.20944/preprints202007.0625.v1

Cited by 6 publications

(1 citation statement)

References 19 publications

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“…It has always been critical to investigate effective humancomputer interaction (HCI) in order to create novel ideas and techniques. Numerous approaches run into issues such as occlusions, changing illumination, limited resolution, and a high frame rate [15].…”

Section: Related Workmentioning

confidence: 99%

Single Shot Detector CNN and Deep Dilated Masks for Vision-Based Hand Gesture Recognition From Video Sequences

Farid,

Hashim,

Abdullah

et al. 2024

IEEE Access

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With an increasing number of people on the planet today, innovative human-computer interaction technologies and approaches may be employed to assist individuals in leading more fulfilling lives. Gesture-based technology has the potential to improve the safety and well-being of impaired people, as well as the general population. Recognizing gestures from video streams is a difficult problem because of the large degree of variation in the characteristics of each motion across individuals. In this article, we propose applying deep learning methods to recognize automated hand gestures using RGB and depth data. To train neural networks to detect hand gestures, any of these forms of data may be utilized. Gesturebased interfaces are more natural, intuitive, and straightforward. Earlier study attempted to characterize hand motions in a number of contexts. Our technique is evaluated using a vision-based gesture recognition system. In our suggested technique, image collection starts with RGB video and depth information captured with the Kinect sensor and is followed by tracking the hand using a single shot detector Convolutional Neural Network (SSD-CNN). When the kernel is applied, it creates an output value at each of the m × n locations. Using a collection of convolutional filters, each new feature layer generates a defined set of gesture detection predictions. After that, we perform deep dilation to make the gesture in the image masks more visible. Finally, hand gestures have been detected using the well-known classification technique SVM. Using deep learning we recognize hand gestures with higher accuracy of 93.68% in RGB passage, 83.45% in the depth passage, and 90.61% in RGB-D conjunction on the SKIG dataset compared to the state-of-theart. In the context of our own created Different Camera Orientation Gesture (DCOG) dataset we got higher accuracy of 92.78% in RGB passage, 79.55% in the depth passage, and 88.56% in RGB-D conjunction for the gestures collected in 0-degree angle. Moreover, the framework intends to use unique methodologies to construct a superior vision-based hand gesture recognition system.

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Section: Related Workmentioning

confidence: 99%

Single Shot Detector CNN and Deep Dilated Masks for Vision-Based Hand Gesture Recognition From Video Sequences

Farid,

Hashim,

Abdullah

et al. 2024

IEEE Access

View full text Add to dashboard Cite

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Artificial Intelligence for Health

et al. 2021

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Impact of In-Air Gestures on In-Car Task’s Diver Distraction

et al. 2023

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As in-vehicle information systems (IVIS) grow increasingly complex, the demand for innovative artificial intelligence-based interaction methods that enhance cybersecurity becomes more crucial. In-air gestures offer a promising solution due to their intuitiveness and individual uniqueness, potentially improving security in human–computer interactions. However, the impact of in-air gestures on driver distraction during in-vehicle tasks and the scarcity of skeleton-based in-air gesture recognition methods in IVIS remain largely unexplored. To address these challenges, we developed a skeleton-based framework specifically tailored for IVIS that recognizes in-air gestures, classifying them as static or dynamic. Our gesture model, tested on the large-scale AUTSL dataset, demonstrates accuracy comparable to state-of-the-art methods and increased efficiency on mobile devices. In comparative experiments between in-air gestures and touch interactions within a driving simulation environment, we established an evaluation system to assess the driver’s attention level during driving. Our findings indicate that in-air gestures provide a more efficient and less distracting interaction solution for IVIS in multi-goal driving environments, significantly improving driving performance by 65%. The proposed framework can serve as a valuable tool for designing future in-air gesture-based interfaces for IVIS, contributing to enhanced cybersecurity.

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Computer Vision for Elderly Care Based on Hand Gestures

Cited by 6 publications

References 19 publications

Single Shot Detector CNN and Deep Dilated Masks for Vision-Based Hand Gesture Recognition From Video Sequences

Single Shot Detector CNN and Deep Dilated Masks for Vision-Based Hand Gesture Recognition From Video Sequences

Artificial Intelligence for Health

Impact of In-Air Gestures on In-Car Task’s Diver Distraction

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